Day: May 17, 2013

After seeing a Game Boy emulator for the first time, [Thijs] was amazed. A small box with just a handful of electronics that turns a Game Boy cartridge into a file able to be run on an emulator is simply magical. [Thijs] has learned a lot about GB and GBC cartridges in the mean time, but still thinks the only way to really learn something is to roll up your sleeves and get your hands dirty. Thus was born [Thijs]’ Game Boy cartridge dumper, powered by a pair of I2C port expanders and a Raspberry Pi.

In the end, [Thijs] managed to dump the ROMs off the Japanese editions of Pokemon Yellow and Gold in about 13 minutes. This is a much slower transfer rate of 26 minutes per SNES cart in the post that gave [Thijs] the inspiration for this build. Still, [Thijs] will probably be the first to say he’s learned a lot from this build, especially after some problems with dumping the right banks from the cartridge.

This single digit display is an old edge-lit module that [Ty_Eeberfest] has been working with. The modules were built for General Radio Company and have a really huge PCB to control just one digit. [Ty’s] modules didn’t come with that driver board, so he was left with the task of controlling an incandescent bulb for each digit. After a bit of thought he figured it would be much easier to just replace the edge-light bulbs with a set of LEDs.

We’ve seen these exact modules before, referenced in a project that created an edge-lit Nixie tube from scratch. Each digit in the display is made from a piece of acrylic with tiny drill holes which trace out the numerals. The acrylic is bent so that the edge exits out the back of the module where it picks up light from the bulb. [Ty] laid out his circuit board so that each LED was in the same position as the bulb it was replacing. As you can see, his retrofit works like a charm.

Here’s an Android powered pen plotter that does it all. It was built by [Ytai Ben-Tsvi] to take with him to Maker Faire. He’s the creator of IOIO, a hardware interface module designed to communicate with an Android device via USB (host or OTG are both supported).

The physical hardware is simple enough. He draws on a pad of white paper using a felt-tipped marker. Located at the top of the easel are two wheels with stars etched on them. They are reels which spool and dole-out string to control the pen’s movements. The pen tip can be lifted by a ball bearing mounted just below it.

But the project really takes off when you watch [Ytai’s] demonstration. The Android tablet controlling the device captures a picture of an object — in this case it’s a toy truck. The app then processes it using edge detection to establish how to plot the image.

For those of you who might have forgotten, let’s go over the rules of Centurion. The object of the game is for every minute, for 100 minutes, drink a shot of beer. It doesn’t sound like a lot, but after completing the challenge you’ll have had 3 liters of beer (or about eight and a half 12 oz cans) in just under two hours. When [Peter] played Centurion, he found the biggest problem was – understandably – keeping track of the time and who drank what. For an upcoming weekend of drinking, [Peter] decided to solve this problem once and for all with shift registers and seven-segment displays.

[Peter]’s Centurion score box comes in two parts. The first and largest part of the build is the main board housing an ATMega8 microcontroller and a huge two digit seven-segment display to keep track of the countdown until the next shot. Two other boards house eight additional two digit seven-segment displays for each player, incremented every time a player presses a giant arcade button.

The entire build is designed around a small travel case that also holds a large battery for cordless drinking parties. Let’s just hope the project is reasonably water-resistant; we can see a lot of spills happening in the future. Check out the video demo below.

We’ve been following the work of [Andrew Holme] and his homebrew GPS receiver for a while now. A few years ago, [Andrew] built a four-channel GPS receiver from scratch, but apparently that wasn’t enough for him. He expanded his build last year to track up to eight satellites, and this month added a Raspberry Pi for a 12-channel, battery-powered homebrew GPS receiver that has an accuracy of about 3 feet.

The Raspi is attached to an FPGA board that handles the local oscillator, real-time events, and tracks satellites automatically. The Pi handles the difficult but not time-critical math through an SPI interface. Because the Pi is attached to the FPGA through an SPI interface, it can also load up the FPGA with even more custom code, potentially turning this 12-channel receiver into a 16- or 18-channel one.

An LCD display attached to the FPGA board shows the current latitude, longitude, and other miscellaneous data like the number of satellites received. With a large Li-ion battery, the entire system can be powered for about 5 hours; an impressively portable GPS system that rivals the best commercial options out there.

Do you ever wonder why geese always fly together in a V-shape? We’re not asking about the fact that it makes the work load much less for all but the lead goose. We mean how is it that all geese know to form up like this? It’s is the act of flocking, and it’s long been a subject of fascination when it comes to robotics. [Scott Snowden] researched the topic while working on his degree a few years ago. Above you can see the demonstration of the behavior using LEGO Mindstorm robots. That’s certainly interesting and you’ll want to check out the video after the break. But his offering doesn’t end with the demo. He also posted a huge article about his work that will provide days of fascinating reading.

We can’t begin to scratch the surface of all that he covers, but we can give you a quick primer on his Mindstorm (NXT) setup. He uses these three bots along with a central brick (the computer part of the NXT hardware) which communicates with them. This lets him use a wide range of powerful tools like MatLab and Processing to recognize each robot with a top-down camera, passing it data based on info harvested with computer vision. From there it’s a wild ride of modeling the behavior as a set of algorithms.